package openvino

import (
	"fmt"
	"os"
	"time"
)

// Model represents an OpenVINO model for inference
type Model struct {
	config ModelConfig
	// In a real implementation, this would hold the compiled model
	// For now, we'll use a placeholder that can be extended
	modelLoaded bool
	device      string
}

// NewModel creates a new OpenVINO model with the given configuration
func NewModel(config ModelConfig) (*Model, error) {
	// Validate model path
	if config.ModelPath == "" {
		return nil, fmt.Errorf("model path is required")
	}

	// Check if model file exists
	if _, err := os.Stat(config.ModelPath); err != nil {
		return nil, fmt.Errorf("model file not found: %s", config.ModelPath)
	}

	// Validate device type
	if !config.Device.IsValid() {
		return nil, fmt.Errorf("invalid device type: %s", config.Device)
	}

	model := &Model{
		config: config,
	}

	// Load the model
	if err := model.load(); err != nil {
		return nil, err
	}

	return model, nil
}

// load initializes the OpenVINO model
func (m *Model) load() error {
	// In a real implementation, this would:
	// 1. Create an OpenVINO Core instance
	// 2. Read the model from the XML file
	// 3. Compile the model for the specified device
	// 4. Create an inference request

	// For now, we'll simulate the loading process
	device := m.config.Device.String()

	// If device is AUTO, we would let OpenVINO choose the best device
	if m.config.Device == DeviceAuto {
		device = "AUTO"
	}

	m.device = device
	m.modelLoaded = true

	return nil
}

// Infer performs inference on the given image data
func (m *Model) Infer(imageData []byte, threshold float32) (*InferenceResult, error) {
	if !m.modelLoaded {
		return nil, fmt.Errorf("model not loaded")
	}

	startTime := time.Now()

	// In a real implementation, this would:
	// 1. Preprocess the image
	// 2. Set input tensor
	// 3. Run inference
	// 4. Postprocess the output
	// 5. Filter by confidence threshold

	// For now, return a placeholder result
	result := &InferenceResult{
		Detections:    []Detection{},
		ProcessTimeMs: float64(time.Since(startTime).Milliseconds()),
		Device:        m.device,
	}

	return result, nil
}

// GetDevice returns the device being used for inference
func (m *Model) GetDevice() string {
	return m.device
}

// GetConfig returns the model configuration
func (m *Model) GetConfig() ModelConfig {
	return m.config
}

// Close releases the model resources
func (m *Model) Close() error {
	// In a real implementation, this would release OpenVINO resources
	m.modelLoaded = false
	return nil
}

// GetAvailableDevices returns information about available devices
func GetAvailableDevices() ([]DeviceInfo, error) {
	// In a real implementation, this would query OpenVINO for available devices
	// For now, return a placeholder list
	devices := []DeviceInfo{
		{
			Name:      "CPU",
			Type:      "CPU",
			Available: true,
			Properties: map[string]string{
				"description": "Intel CPU",
			},
		},
	}

	return devices, nil
}

// GetOptimalDevice returns the optimal device for inference
func GetOptimalDevice() (DeviceType, error) {
	// In a real implementation, this would query OpenVINO for the best device
	// For now, return AUTO which lets OpenVINO choose
	return DeviceAuto, nil
}

